Damage Identification of Hydraulic Structure Based on Neural Network and Deep Learning

Abstract

Due to the rapid development of science and technology, the scale and difficulty of hydraulic structures such as hydraulic engineering, port engineering, ocean engineering, and hydraulic structures such as bridges and tunnels are increasing, and hydraulic structures are subject to design loads and various environmental factors during use. Affect the safety and stability of the structure, such as material heating, damage accumulation, and other problems. The direct or indirect economic loss caused by failure or sudden damage in use will be very large. Therefore, the realization of structural damage identification and real-time monitoring of important structures is of great significance in theory or in practice. However, due to factors such as damage, overload, earthquake, etc., the structure of hydraulic structures is inevitably damaged, which affects the traffic performance of hydraulic structures. Serious damage will threaten the safety of hydraulic structures. Therefore, it is necessary to grasp the correctness in time. Hydraulic structure and identifying damage to buildings. Deep learning method is an important research result in the field of machine learning in recent years. Compared with traditional pattern recognition methods, it has greater advantages in recognition performance. However, the research on the application of neural network and deep learning methods in the field of hydraulic structure damage identification is still lacking. Therefore, this paper studies the application of neural network and deep learning methods in hydraulic structure damage identification. In this paper, deep learning and neural networks are used to simulate the damage of hydraulic structures, and BP neural network and deep learning are combined to identify hydraulic structures. Satisfactory results are obtained, which shows that this method is suitable for hydraulic structure damage identification. It has certain application value.